Penn Engineering Events

MEAM Ph.D. Thesis: "Geometric Methods for Efficient and Explainable Control of Underactuated Robotic Systems"
2025-07-21 14:00 - 15:00 (America/New_York) | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Robots are complex, high-dimensional systems, governed by nonlinear, underactuated dynamics and evolving on non-Euclidean manifolds, posing numerous challenges for control synthesis and analysis. While optimization-based methods of control can flexibly accommodate diverse dynamics, costs, and constraints, they often demand coarse approximations or powerful onboard processors (infeasible for many aerial and space systems) due to their relatively poor computational efficiency. Although learned controllers can generally cope with more moderate onboard resources, the computational burden of offline training is heavy, and both the training pipeline and the policy obtained are often brittle. Conversely, explicit control laws designed analytically often have miniscule computational overhead and perform robustly, but they are typically only applicable to individual systems or a narrow class, limiting their broader usefulness.
Nonetheless, robots are not black-box nonlinear control systems—rather, their dynamics enjoy powerful properties (e.g., symmetry and mechanical structure) that can be leveraged to gain traction on control design problems. In this thesis, we explore the role of geometric methods in mitigating many of the above drawbacks, across both analytical and data-driven methods. We study the role of symmetry in identifying effective abstractions for trajectory planning in underactuated mechanical systems (in particular, “flat outputs”) and explore applications to task space planning for aerial manipulation. We also develop methods for synthesizing tracking controllers for mechanical systems evolving on the general class of homogeneous Riemannian manifolds, and give certificates for the almost global asymptotic stability of cascades, which often appear in the closed-loop dynamics of hierarchical controllers for underactuated systems. Lastly, we leverage symmetry to accelerate training of tracking controllers via reinforcement learning (by constructing “continuous MDP homomorphisms”), also improving converged performance.
In all these methods, a geometric perspective enables us to explainably construct abstractions that reduce dimensionality, enforce structure, and capture essential properties, all the while representing the system or problem in a form more convenient for analysis or design. In contrast to ad hoc methods, such reduced representations typically improve computational efficiency, while also encouraging generality over a broader class of systems and affording insight into why prior handcrafted approaches were successful for particular cases. Sometimes, such realizations also guide mechanical design, closing the control-morphology feedback loop and leading to synergies between a robot’s embodiment and its controller. By combining explainable abstractions with scalable computation, such methods build towards a future in which robotic systems move through their surroundings as capably and dynamically as their counterparts in Nature.

MEAM Seminar: "Exploring Jet-Propelled Soft Robots: Design, Experiments, and Theory"
2025-07-22 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Understanding how marine animals migrate is critical for assessing the impacts of climate change on ocean ecosystems—and yet current Autonomous Underwater Vehicles (AUVs), with their noisy propellers and rigid hulls, are ill-suited to operate alongside sensitive species. Bio-inspired robots offer a promising alternative by emulating the natural locomotion strategies of fish, cephalopods, and other marine organisms; however, most existing prototypes still fall short of their biological counterparts in speed, and energy efficiency—highlighting a significant performance gap.
In this talk, I will focus on one specific locomotion—jet propulsion—and present our efforts to narrow that gap. First, I will introduce our squid-inspired underwater robotic system and its evolution over the past few years, discussing experiments and theoretical models that show how design and control parameters influence its performance. Next, building on the squid-inspired robot platform, we developed a salp-inspired robot with an additional frontal nozzle; experiments are conducted between the two robots to compare their performances and theoretical explanations are proposed to address the differences. Finally, I will talk about our recently developed swivel-nozzle steering mechanism and describe our plan for achieving controlled two-dimensional navigation.

ESE Guest Seminar: "On-Device Probabilistic AI: From Gaussian Transistors to Light-Driven Spike Encoding"
2025-07-29 11:30 - 12:30 | Room 221, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Emerging edge AI systems call for device-level approaches that are inherently low-power, secure, and capable of managing uncertainty. In this talk, I will share our recent exploratory efforts toward realizing on-device probabilistic intelligence using custom-designed semiconductor devices. I will introduce Gaussian transistors that support analog Gaussian activation and probabilistic inference by harnessing device-level variability. These devices offer a potential path for implementing Bayesian operations directly at the transistor level. In parallel, we have been developing photo-spike photodetectors that convert light fluctuations into asynchronous spike trains, functioning as both neuromorphic input interfaces and entropy sources for physical randomness. While still in early stages, the combination of these platforms suggests a promising direction for hardware-embedded probabilistic learning, secure classification, and physical random number generation. This work aims to show how tuning the physics of emerging devices may open up new opportunities for edge AI systems.

ESE Ph.D. Thesis Defense: "Novel van der Waals Chalcogenides for Sustainable Light Harvesting"
2025-07-30 09:00 | Raisler Lounge (Room 225), Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
The global climate crisis demands a shift to renewable energy sources. Solar photovoltaics (PVs) are widely considered the most feasible renewable technology to meet global energy demands, and solar photo-electrocatalysis is a promising approach to decarbonize industrial chemical production. However, scaling solar energy harvesting technologies to meet energy demands must be done economically and sustainably, minimizing materials consumption, toxicity, energy intensity of the processing, and cost per watt.
My research aims to leverage the strong light-matter interaction of van der Waals (vdW) chalcogenides for solar energy harvesting with drastically reduced materials consumption while also developing low-cost solution processing of elemental vdW chalcogenides for PVs. In this defense, I present work to (i) engineer vdW metal dichalcogenide nanophotonic structures to achieve broadband near unity solar absorption in extremely thin (18 nm) layers; (ii) apply hybrid light-matter states sustained by thin films of vdW metal dichalcogenides to PVs; and (iii) develop a precursor and process to fabricate thin film elemental chalcogenide PVs with widely tunable bandgaps from solution phase for low cost, low temperature manufacturing without extremely hazardous solvents. Overall, these contributions offer potential paths for materials processing and optical design to make future solar energy technologies more sustainable.

MEAM Seminar: "Leveraging Robot-Based Haptic Dyads to Improve Community-Based Stroke Rehabilitation"
2025-08-05 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Disabilities related to aging and stroke impact functional independence and quality of life for millions of older adults, creating a growing need for scalable, accessible rehabilitation solutions. Community-based robotic therapy that leverages social interaction and haptic feedback offers a promising approach, particularly for individuals with motor and cognitive impairments.
This seminar presents work exploring how haptic interaction between individuals influences motor learning and usability in a rehabilitation context. I will begin with findings from a literature review on robot-based haptic dyads, highlighting how haptic connections have previously been used to study motor learning in healthy young adults. I will then present our design for a low-cost robotic rehabilitation system to haptically connect multiple users. Next, I will discuss preliminary results from a pilot study and full experimental protocol involving healthy older adults and stroke survivors, comparing individual and partnered motor learning in a robot-based tracking task. Analyses include performance outcomes, motor learning curves, and user-reported experience across conditions. I will also introduce a computational model of solo human-robot interaction, based on inverse optimal control, to simulate impaired sensorimotor behavior in a simplified robot-based assessment task.
Together, these studies provide a foundation for understanding how impairment shapes sensorimotor learning and how robot-based haptic dyads can be designed to support recovery. Future work will build on this foundation to model dyadic interaction strategies and implement adaptive controllers that balance partner abilities in collaborative rehabilitation tasks, with the ultimate goal of enabling personalized, socially engaging robot-based therapy.

[VIRTUAL SPEAKER]: Summer 2025 GRASP Seminar: Michal Gregor, Kempelen Institute of Intelligent Technologies, "Low-Resource NLP: Not Just Throwing Data at a Model and Hoping for the Best"
2025-08-06 11:00 - 12:00 | Virtual via Zoom
This is a virtual event ONLY with attendance via Zoom.
ABSTRACT
The talk will introduce several topics in low-resource and multilingual NLP – in the domain of disinformation combatting, through works done at the Kempelen Institute of Intelligent Technologies in Bratislava – and also in the more general context of efficiently adapting large language models to smaller languages. It will argue that machine learning – even in the era of deep learning and large language models – is not just about throwing increasing amounts of data at a model and hoping for the best; our lack of understanding can and sometimes does severely limit the capabilities of our models.

CBE Doctoral Dissertation Defense: "Quantitative Transcriptional Regulation through Protein–DNA Interactions in Developing Systems" (Gaochen Jin)
2025-08-07 10:00 - 12:00 | Towne 225
Abstract:
Precise regulation of gene expression is essential for controlling developmental programs, maintaining cellular identity, and ensuring proper tissue function. Dynamic interactions between proteins and cis-regulatory elements integrate molecular mechanisms and extracellular signaling to achieve precise control of transcriptional activity. In this thesis, I investigate protein-DNA-mediated transcriptional regulation across two distinct developmental systems: mouse embryonic stem cells (mESCs) and early Drosophila embryos.
Using PP7/PCP live-cell imaging, I tracked Sox2 transcriptional activity in single mESCs under LIF pathway perturbations (Chapter 2). Removing LIF ligand or inhibiting JAK signaling induced heterogeneous changes in Sox 2 activity, reducing the number of Sox2-active cells. Transcriptional output in remaining Sox2-active cells decreased, caused by smaller and less frequent transcriptional bursts. LIF perturbation also decreased the number of pluripotent cells, with pluripotent marker-positive cells showing higher Sox2 mRNA production. Moreover, Sox2 transcription displayed transcriptional memory, with active mother cells more likely to reactivate Sox2 in daughter cells, even under signaling disruption. These findings reveal quantitative aspects of Sox2 regulation essential for pluripotency maintenance.
In early Drosophila embryos, I investigated how the dosage of the transcription factor Dorsal (Dl) and TF binding sites affinity govern the spatial and temporal regulation of the snail (sna) gene (Chapter 3). Surprisingly, reducing the level of Dl, normally an activator of sna, led to increased sna transcriptional activity. This inverted dosage effect is mediated by the autoregulation of the Sna repressor. Reduced Dl initially decreases Sna protein production, which in turn reduces autorepressive feedback on the sna gene, leading to compensatory increases in sna transcription. Increasing Dl binding sites affinity within sna enhancers also reduced sna transcriptional activity and altered bursting behavior. Finally, we showed that Sna-mediated autorepression modulates enhancer responsiveness in a dosage- and context-dependent manner.
Together, this work reveals how transcriptional feedback mechanisms can modulate gene expression outputs beyond the direct effects of TF input levels. Together, these studies demonstrate how examining gene regulatory dynamics across distinct biological systems can uncover fundamental principles of transcriptional control and inform strategies for targeted modulation of gene expression in biomedical research.
Zoom Information:
Meeting ID: 925 5127 1427
Passcode: [REDACTED]

MEAM Seminar: "Predicting Infant Center of Pressure through Physics and Data Driven Modeling"
2025-08-12 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Affecting roughly 2 in 1000 infants in the USA, Cerebral Palsy (CP) is the most common cause of motor impairment in children. CP has no cure, but motor therapy is an effective tool for providing rehabilitation. Although therapy is most effective before the age of 2, early CP detection is difficult and labor-intensive, making the processes inaccessible in low-resource settings. To remedy this inaccessibility, we seek to create an accessible technology-based tool to make detecting neuromotor impairment in infants less easier. Studies have shown promising results in quantifying infant impairment by observing changes in the Center of Pressure (COP), as they lie supine. Although a useful metric, the force plates necessary to capture COP are often not readily accessible in low-resource settings due to factors such as high price and a lack of portability. In response, my goal is to make COP easier to obtain by predicting supine infant COP through human pose data gathered with cameras. I seek to derive a generalized physics-based model of the infant’s dynamics that calculates COP based on insights gained from examining how infant movement interacts with changes in COP. I will then use this physics-based model to improve the ability to use machine learning to derive COP from only camera information. As a result, I will create a novel framework involving the use of physics-based modeling and data-driven modeling to predict COP.

MEAM Seminar: "Temperature Dependent Offset for Spin Lattice Dynamics Modeling"
2025-08-19 10:15 - 11:15 | Room 337, Towne Building, 220 South 33rd Street, Philadelphia, PA, 19104, United States
Spin lattice dynamics (SLD) modeling is an approach to modeling magnetic interactions at the nanoscale. Current spin lattice dynamics models do not model magnetoelastic effects well, giving non-physical lattice parameters and poorly modeling behavior like forced volume magnetostriction. A new model, based on a temperature dependent offset that subtracts out the part of the magnetic interactions accounted for in the lattice potential to avoid double counting of forces, has been developed to address these shortcomings. The current model matches lattice parameter for no field applied conditions. Currently, forced volume magnetostriction is being studied with the model.

AI + Science Seminar
2025-08-26 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Extracting Knowledge Priors from Scientific Texts for De Novo Molecular Design
This talk will explore a large-scale effort to exploit the natural-language understanding capabilities of large language models in order to unlock information about the structure, function, and biological activity for proteins, small molecules, genetic variants, and other biological entities of interest.
Register Now

ASSET Seminar: "AIRFoundry: AI for RNA Discovery and Synthesis"
2025-08-27 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Virtually everyone today is aware of the mRNA vaccine for COVID, which saved many lives and earned the Nobel Prize for Drs. Drew Weissman and Katalin Kariko. The vaccine consisted not only of mRNA that coded for the COVID spike protein, but a protective lipid nanoparticle envelope that carried the RNA to cells in the body. RNA has immense potential beyond vaccines, for instance, as a way of stimulating cells to manufacture particular proteins; silencing certain genes; altering gene expression; and targeting CRISPR/CAS gene editing.
The mission of the AIRFoundry is to offer an AI-guided facility for biologists, veterinarians, biomedical researchers, and industry to develop and synthesize RNA and LNPs appropriate for different therapeutic uses. This exceeds the capabilities of existing scientific question-answering tools, and needs to be done while simultaneously “bootstrapping” a community of users. In this talk, I will describe our early experiences and innovations in developing AI question answering and “lead discovery” tools for promising candidate RNAs and LNPs — even as our collaborators build out tools for RNA and LNP optimization, and robot-driven synthesis.
Collaborative work with Daeyeon Lee, David Issadore, Drew Weissman, Claribel Acevedo-Velez, Joost Wagenaar, Sharath Guntuku, Rodolfo Ramanach, Masoud Soroush, Jake Gardner, Mark Yatskar, Jiaming Liang, Varun Jana, Haydn Jones, and John Frommeyer.

Seminar Recording: https://drive.google.com/file/d/1jsi-4eQoiPYg9u-ivvh4KflLC6oTY7Mr/view?usp=drive_link

Entrepreneurship Expo 2025
2025-08-27 17:00 - 19:00 | Stat
On Wednesday, August 27, from 5:00 PM to 7:00 PM ET, Venture Lab is hosting the 2025 Entrepreneurship Expo, a one-stop experience for students to explore co-curricular programs, discover entrepreneurship clubs, and learn how to take the next step in their entrepreneurial journey.
From students who are curious about innovation to those actively launching ventures, the Expo offers pathways for skill-building, mentorship, funding, and more. It’s also a great way for students to network and get inspired by the broader entrepreneurial ecosystem at Penn.

Who’s invited: This event is ONLY open to active Penn students and special guests.
Location: 115 South 40th Street
When: Wednesday, August 27, from 5:00 PM to 7:00 PM ET

Register Now

CBE Doctoral Dissertation Defense: "Silica Nanoparticle-Based Platform for Rare Earth Element Recovery and Separation" (Ivy Dai)
2025-08-29 11:00 - 13:30 | Vagelos Institute for Energy Science and Technology, Room 121, 231 S 34th Street, Philadelphia, PA, 19104, United States
Abstract:
Rare earth elements (REEs) are essential for clean energy technologies including batteries for electrical vehicles, wind turbines and LED screens due to their unique magnetic, optical, and electronic properties. Efficient methods for their recovery and separation are urgently needed to meet the growing demands of a rapidly evolving green economy worldwide. However, current separation technologies, such as liquid-liquid extraction (LLE), suffer from harmful environmental impacts, scalability limitations and high energy costs, due to the similar physiochemical properties of REEs and the dependence on organic solvents. These environmental, operational, and economic challenges motivate the development of sustainable, selective and scalable REE separations.
Nanostructured materials, such as those incorporating silica nanoparticles (SiO 2 NPs), are promising materials to incorporate into REE separations due to their high surface area, tunable surface chemistry and environmental compatibility. Their scale offers several advantages, including high surface-to-volume ratio, integration into dynamic architectures, and stabilization of complex structures. These features offer new opportunities for designing alternative methods for REE recovery and separation methods which do not have the drawbacks of existing approaches.
This thesis focuses on developing a SiO 2 NP-based platform for REE separation, building from fundamental understanding to practical applications. We investigate the fundamental interactions between SiO 2 NPs and REEs across the full pH range of pH 3 – 10, identifying and mapping the transition of three distinct interaction modes with a combinational of technical tools. We demonstrate the intrinsic, size dependent selectivity, with SiO 2 NPs favoring smaller, more charge dense heavy REEs (HREEs) over larger light REEs (LREEs) in both binary and ternary mixtures under competitive conditions. Our results also show reversible adsorption of REEs on SiO 2 NP surfaces, enabling ligand-free separation processes.
Building upon this mechanistic understanding, we integrate the SiO 2 NP-based platform into three separation processes: (i) solid phase extraction, where SiO 2 NPs act as active adsorbents that enable size-dependent selectivity and reversible capture and release via simple pH-swings, (ii) froth flotation, where SiO 2 NPs serve as REE carrier and foam stabilizer, and (iii) bicontinuous interfacially jammed emulsion gels, where the nanoparticle-stabilized interfaces enable high interfacial area for REE adsorption and extractant loading. This work provides a foundation for developing sustainable REE separation strategies with nanoparticles.

MEAM Seminar: "Harnessing Living Bacteria, Fungi, and Plants as Functional Materials"
2025-09-02 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
For decades materials engineers have been inspired by biology to create improved properties, for example nacre-inspired hierarchical structures to improve toughness, tree-inspired vasculature to deliver fluids that can react and heal an interface, and mussel-inspired strong underwater adhesives utilizing cation-pi interactions. Now, the bioengineering toolset has reached a maturity where we can start to employ biology directly as materials for the built environment – the mission of the Engineered Living Materials Institute at Cornell. In this seminar, I will highlight several recent projects and then focus on our design and validation of a hybrid living coating that can be applied to conventional structural materials for crack detection. I will describe the development of a bacterial spore–polymer composite coating that enables in situ detection of cracks under different loading modes, geometries, and substrate materials. Excitingly, this biohybrid coating approach unlocks potential, beyond crack detection, for crack mitigation by leveraging the biological component.

Penn AI Seminar Series: Geometric and Physics Stucture Preservation in Scientific Machine Learning
2025-09-02 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Geometric and Physics Stucture Preservation in Scientific Machine Learning
Nat Trask, Associate Professor in Mechanical Engineering and Applied Mechanics at Penn Engineering, will share about his work constructing real-time digital twins built upon a data-driven finite element exterior calculus; constructing auto-regressive integrators with guaranteed long-term stability independent of rollout length; and constructing data-driven particle models built upon metriplectic bracket theory, which preserve emergent statistical mechanics.
Lunch will be served.
Registration is required. Sign up here.

ASSET Seminar: "Provable vs Impossible Trust: Reasoning, Steering, and Safety"
2025-09-03 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
Abstract: In this talk, I will discuss a collection of highlights from our recent work in trustworthy AI.
(1) Certifying reasoning explanations with reliability guarantees and aligning with expert knowledge,
(2) Simple yet effective steering inspired from theoretical rule-following mechanisms for transformers, and
(3) The impossibility of monitoring stateless attackers and what safety defenses should be doing.

Seminar Recording: https://drive.google.com/file/d/1FNeVVPXb_vZiNWVexFTgTFoVKBM_QnqQ/view?usp=sharing

CBE Seminar: "Water–Hydrophobe Interfaces: From Debunking Myths to Boosting Global Food–Water–Climate Resilience" (Himanshu Mishra, KAUST)
2025-09-03 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
First, I will outline our work on overcoming barriers to desert rehabilitation for urban greening, landscaping, and regional food security. In arid regions, freshwater is scarce, sandy soils lose water and fertilizers rapidly, which stifles plant growth. Our team has developed two complementary solutions: (i) Superhydrophobic Sand (SHS)1 — a plastic-free, bio-inspired mulch that cuts evaporative water loss from topsoil by ~80%; (ii) CarboSoil2 — an engineered biochar that greatly enhances nutrient and water retention in sandy soils. I will share our translational journey from materials invention and characterization to multi-year field trials with food crops and native plants in collaboration with industry partners. Results show SHS and CarboSoil can dramatically improve irrigation and fertilizer-use efficiency in hot, dry, sandy environments. Terraxy LLC, the startup I co-founded, is now scaling Carbosoil production to 6,000 tons/year for regional sustainability projects (video link).
Next, I will discuss our investigation into controversial claims that hydrogen peroxide (H₂O₂) forms spontaneously at the air–water interface in microdroplets3-5 — a phenomenon that would upend fundamental physical chemistry and impact fields from cloud chemistry to green synthesis. We tested H₂O₂ formation in sprayed and condensed microdroplets under varied flow rates, air compositions, pH, and salt content. Our findings show that the effect is not due to the air–water interface or microdroplet geometry6. Instead, H₂O₂ originates from the reduction of dissolved oxygen at the solid–water interface7. Within a 50 nM detection limit, no H₂O₂ was detected in oxygen-free conditions (more here).

References

A. Gallo, K. Odokonyero, M. A. A. Mousa, J. Reihmer, S. Al-Mashharawi, R. Marasco, E. Manalastas, M. J. L. Morton, D. Daffonchio, M. F. McCabe, M. Tester and H. Mishra, ACS Agricultural Science & Technology, 2022, 2, 276-288.
K. Odokonyero, B. Vernooij, B. Albar, L. O. Exposito, A. Alsamdani, A. A. G. Haider, N. V. H. Musskopf, N. Kharbatia, A. Gallo and H. Mishra, Frontiers in Plant Science, 2024, Volume 15 – 2024.
M. A. Mehrgardi, M. Mofidfar and R. N. Zare, Journal of the American Chemical Society, 2022, 144, 7606-7609.
J. K. Lee, K. L. Walker, H. S. Han, J. Kang, F. B. Prinz, R. M. Waymouth, H. G. Nam and R. N. Zare, P Natl Acad Sci USA, 2019, 116, 19294-19298.
R. A. LaCour, J. P. Heindel, R. Zhao and T. Head-Gordon, J Am Chem Soc, 2025, 147, 6299-6317.
N. H. Musskopf, A. Gallo, P. Zhang, J. Petry and H. Mishra, The Journal of Physical Chemistry Letters, 2021, 12, 11422-11429.
M. A. Eatoo and H. Mishra, Chem Sci, 2024, 15, 3093-3103.

FOLDS Seminar: Positive random walks and positive-semidefinite random matrices
2025-09-04 12:00 - 13:00 | Amy Gutmann Hall, Room 306, 3317 Chestnut Street, Philadelphia, PA, 19104, United States
On the real line, a random walk that can only move in the positive direction is very unlikely to remain close to its starting point. After a fixed number of steps, the left tail has a Gaussian profile, under minimal assumptions. Remarkably, the same phenomenon occurs when we consider a positive random walk on the cone of positive-semidefinite matrices. After a fixed number of steps, the minimum eigenvalue is also described by a Gaussian model.
This talk introduces a new way to make this intuition rigorous. The methodology provides the solution to an open problem in computational mathematics about sparse random embeddings. The presentation is targeted at a general mathematical audience.

Zoom link: https://upenn.zoom.us/j/98220304722

BE Doctoral Dissertation Defense: Aoife O'Farrell
2025-09-04 14:00 - 15:00
The Department of Bioengineering at the University of Pennsylvania and Dr. Arjun Raj are pleased to announce the Doctoral Dissertation Defense of Aoife O’Farrell.
Title: Stimulus Specificity of Trained Immune Memory in Human Macrophages
Advisor: Dr. Arjun Raj
Date and Time: Thursday, September 4th at 2:00 PM
Location: Smilow 12-146AB
Zoom Link: https://upenn.zoom.us/j/94438324577
The public is welcome to attend.

BE Doctoral Dissertation Defense: Michael Yao
2025-09-05 11:00 - 12:00
The Department of Bioengineering at the University of Pennsylvania and Drs. Osbert Bastani and James Gee are pleased to announce the Doctoral Dissertation Defense of Michael Yao.
Title: Distributionally Robust Machine Intelligence
Advisors: Osbert Bastani, PhD and James Gee, PhD
Date & Time: Friday, September 5th at 11am
Location: Amy Gutmann Hall, Room 414 (3317 Chestnut St, Philadelphia, PA 19104)
RSVP Link: https://www.michaelsyao.com/defense
Zoom Link: https://pennmedicine.zoom.us/j/93390061157?pwd=[REDACTED]
The public is welcome to attend.

Future Proof Your Research With Rigor
2025-09-08 15:00 - 16:00 | Bodek Lounge, Houston Hall, 3417 Spruce St, Philadelphia, PA, 19104, United States
What’s the best way to avoid paper retractions and irreproducible results? Conduct rigorous research. Join us for a public talk with Ivan Oransky, Co-Founder of Retraction Watch, Anita Anita Bandrowski, founder of The Research Resource Identification Initiative (RRID), and Jason Williams, Assisant Director of the DNA Learning Center, Cold Spring Harbor Laboratory, on doing science that stands the test of time—and scrutiny.
Houston Hall – Bodek Lounge, 3417 Spruce Street Philadelphia, PA 19104
RSVP here to save your spot!

*This event is part of the C4R25 Conference – Sep 8-10 in Philadelphia – the world’s only conference dedicated to research rigor! Learn more at c4r.io/conference

MEAM Seminar: MEAM Faculty Showcase
2025-09-09 10:15 - 11:15 | Wu and Chen Auditorium (Room 101), Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104, United States
Please join us on Tuesday, September 9 for an overview of research being done in the MEAM Department, hosted by MEAM Department Chair, Dr. Kevin Turner. This is an excellent opportunity for current graduate students to learn about the breadth of work being done in MEAM. The following faculty will be presenting (not in order of presentation):

ASSET Seminar: "Rethinking Test-Time Thinking: From Token-Level Rewards to Robust Generative Agents"
2025-09-10 12:00 - 13:15 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
We present a unified perspective on test-time thinking as a lens for improving generative AI agents through finer-grained reward modeling, data-centric reasoning, and robust alignment. Beginning with GenARM, we introduce an inductive bias for denser, token-level reward modeling that guides generation during decoding, enabling token-level alignment without retraining. While GenARM targets reward design, ThinkLite-VL focuses on the data side of reasoning. It proposes a self-improvement framework that selects the most informative samples via MCTS-guided search, yielding stronger visual reasoning with fewer labels. Taking this a step further, MORSE-500 moves beyond selection to creation: it programmatically generates targeted, controllable multimodal data to systematically probe and stress-test models’ reasoning abilities. We then interrogate a central assumption in inference-time alignment: Does Thinking More Always Help? Our findings reveal that increased reasoning steps can degrade performance–not due to better or worse reasoning per se, but due to rising variance in outputs, challenging the naive scaling paradigm. Finally, AegisLLM applies test-time thinking in the service of security, using an agentic, multi-perspective framework to defend against jailbreaks, prompt injections, and unlearning attacks–all at inference time. Together, these works chart a path toward generative agents that are not only more capable, but more data-efficient, introspective, and robust in real-world deployment.

Seminar Recording: https://drive.google.com/file/d/13jOKuou0QzqkMo9QHEdoHA1nCIxOPsbm/view?usp=drive_link

Fall 2025 GRASP SFI: Tairan He, Carnegie Mellon University & NVIDIA, “Scalable Sim-to-Real Learning for General-Purpose Humanoid Skills”
2025-09-10 15:00 - 16:00 | Levine 307, 3330 Walnut Street, Philadelphia, PA, 19104, United States
This is a hybrid event with in-person attendance in Levine 307 and virtual attendance via Zoom.
ABSTRACT
Humanoids represent the most versatile robotic platform, capable of walking, manipulating, and collaborating with people in human-centered environments. Yet, despite recent advances, building humanoids that can operate reliably in the real world remains a fundamental challenge. Progress has been hindered by difficulties in whole-body control, robust perceptive reasoning, and bridging the sim-to-real gap.

In this talk, I will discuss how scalable simulation and learning can systematically overcome these barriers. I will present a research trajectory that advances humanoid capabilities along three dimensions:

1. Sim-to-Real Control: From real-time teleoperation (H2O) to dexterous loco-manipulation (OmniH2O), to a versatile generalist controller (HOVER), and agile transfer via dynamics alignment (ASAP), these works demonstrate increasingly dexterous and adaptable control.
2. Sim-to-Real Perception: With ABS, we show that robust real-world locomotion requires tightly coupling exteroceptive and proprioceptive sensing—shifting sim-to-real learning from blind skill execution to perception-driven control.
3. Future Directions: I will outline potential next steps in (1) Perceptive Loco-Manipulation: end-to-end visuomotor policies unifying perception, locomotion, and manipulation—and (2) Real-to-Sim Evaluation: using high-fidelity simulators environments to provide consistent evaluation protocols and benchmarks for real-world policy evaluation.

Taken together, these works explore how far scalable sim-to-real learning can advance humanoid capabilities across control and perception. While sim-to-real is not the only path toward reliable humanoids, this talk aims to test its limits—probing how scaling simulation and learning can push humanoids closer to functioning as capable partners in real-world environments.

CBE Seminar: "Non-equilibrium Dynamics of Lipid Vesicles using Automated Flow Control" (Charles Schroeder, Princeton University)
2025-09-10 15:30 - 16:30 | Wu & Chen Auditorium
Abstract:
Vesicles are membrane-bound compartments that play a central role in biology. Despite recent progress, the dynamics of single- and multi-component lipid vesicles are not fully understood, particularly far from equilibrium where complex nonspherical shapes undergo large deformations in flow. In this talk, I will present recent work from our group on the non-equilibrium dynamics of lipid vesicles in precisely defined flows using the Stokes trap – a new method that enables full 3D control of position and orientation of molecules or particles using active feedback control, without the need for external optical, magnetic, or electric fields. After characterizing equilibrium properties including bending modulus and membrane tension, we study vesicle deformation as a function of dimensionless flow strength (capillary number, Ca) and vesicle deflation (reduced volume). Our results show that vesicles are remarkably deformable, exhibiting reversible shape changes with aspect ratios exceeding 20 in repeated stretch-relax cycles in the bending-dominated regime. Single-component vesicles show a rich variety of shapes and conformations, including asymmetric and symmetric dumbbells, in addition to pearling, wrinkling, and buckling instabilities, depending on membrane properties. Based on these observations, we construct a detailed flow-phase diagram for vesicles in extensional flow, and we further analyze transient stretching and relaxation dynamics. Two distinct relaxation processes emerge for deformed vesicles, including a fast relaxation process corresponding to bending modes and a slow process governed by membrane tension relaxation. Finally, we study vesicle shape dynamics in time-dependent large-amplitude oscillatory flows, revealing three distinct dynamical regimes – pulsating, reorienting, and symmetrical deformations – arising from the competition between flow and membrane deformation timescales. Together, these results provide new insight into flow-driven shape instabilities for lipid vesicles using new methods in flow automation.

FOLDS seminar: Algorithmic stability for regression and classification
2025-09-11 12:00 - 13:00 | Amy Gutmann Hall, Room 414, 3333 Chestnut Street, Philadelphia, 19104, United States
In a supervised learning setting, a model fitting algorithm is unstable if small perturbations to the input (the training data) can often lead to large perturbations in the output (say, predictions returned by the fitted model). Algorithmic stability is a desirable property with many important implications such as generalization and robustness, but testing the stability property empirically is known to be impossible in the setting of complex black-box models. In this work, we establish that bagging any black-box regression algorithm automatically ensures that stability holds, with no assumptions on the algorithm or the data. Furthermore, we construct a new framework for defining stability in the context of classification, and show that using bagging to estimate our uncertainty about the output label will again allow stability guarantees for any black-box model. This work is joint with Jake Soloff and Rebecca Willett.

Zoom link: https://upenn.zoom.us/j/98220304722

ESE PhD Thesis Defense - "Design and Characterization of AlScN-Based Ferroelectric Devices for Non-Volatile Memory Applications"
2025-09-11 15:00 - 17:00 | Room 221, Singh Center for Nanotechnology, 3205 Walnut Street, Philadelphia, PA, 19104, United States
Ferroelectric materials have emerged as promising candidates for next-generation non-volatile memory technologies due to their intrinsic remnant polarization and fast switching dynamics. Among them, aluminum scandium nitride (AlScN) stands out for its CMOS compatibility, low growth temperature, exceptional thermal stability and high remnant polarization compared to conventional perovskite and hafnia-based ferroelectrics.
In this dissertation, I present a comprehensive study of AlScN-based ferroelectric devices for non-volatile memory applications. I first demonstrate ferroelectric field-effect transistors (FeFETs) by integrating AlScN with 2D materials, where the Sc composition in AlScN is controlled. Through systematic electrical characterization, including current–voltage (I–V), polarization–voltage (P–V) loops, and pulsed positive up negative down (PUND) measurements, I elucidate device physics, endurance, and wake-up/fatigue behavior. By engineering the contact interface, I further realize n-type, p-type, and ambipolar FeFETs with enhanced ferroelectric gating efficiency and substantially improved on-state current.
Next, I investigate highly scaled AlScN-based ferroelectric diodes (Fe-diodes) and highlight their potential for high-density memory integration. Taken together, these studies establish AlScN as a robust material platform for ferroelectric device technologies, bridging fundamental materials science with device- and circuit-level opportunities in memory and beyond.

ESE PhD Thesis Defense - "Learning-based Safe and Robust Control for Multi-Agent Systems"
2025-09-12 12:00 - 13:30 | WYSIWYG
AI-enabled systems have become ubiquitous and integral to safety-critical domains, e.g., autonomous vehicles and aerial robotics. Despite promising empirical results, decision-making processes for critical systems incorporating AI components require careful consideration, as failures may have catastrophic consequences. One key challenge is that various uncertainties will inevitably arise from system limitations, black-box models, or environmental factors, and inaccurate estimation of intrinsic uncertainties or failure to account for other agents in the environment can lead to hazardous behaviors.
In this dissertation, we study how to develop safe and robust learning-based control policies under various uncertainties. In particular, it explores how tools from statistics, game theory and formal methods can empower uncertainty quantification, adaptation to other agents, and robust policy synthesis. The first part focuses on safe learning and control multi-agent systems, where we show how to develop safe, robust, and adaptive control strategies in safety-critical systems when encountering other agents. The second part studies how to synthesize safe perception-based control policy for robotic systems under uncertainties.

ESE PhD Thesis Defense - "Magnetostatic Surface Wave and Surface Acoustic Wave Devices for Tunable and Energy Efficient Radio Frequency Filters"
2025-09-12 13:00 - 14:00 | Greenberg Lounge (Room 114), Skirkanich Hall, 210 South 33rd Street, Philadelphia, PA, 19104, United States
Tunable and energy-efficient filters are key components in modern wireless communication, where RF front-end systems must operate across multiple frequency bands while minimizing power consumption.
This thesis focuses on the design and fabrication of miniature, narrowband, tunable bandpass and bandstop filters based on magnetostatic waves (MSW) in yttrium iron garnet (YIG) waveguides. A zero-static-power magnetic bias circuit is used to tune the filter’s center frequency, but the compact size of the magnetic bias circuit imposes stringent limits on the YIG waveguide dimensions. To address this challenge, microfabricated YIG thin films with aluminum meander-line transducers were developed. These designs improve the resonator figure of merit, reduce insertion loss, and enhance coupling. Increasing the YIG thickness further improves skirt selectivity, lowers propagation loss, and increases power handling. By integrating wideband tunability with nonreciprocal behavior, a single device can replace multiple RF switches, filters, and isolators, simultaneously controlling passband selection and directional isolation. This integration simplifies RF front-end design by reducing the number of required components.
In parallel, this thesis introduces a high-frequency surface acoustic wave (SAW) platform using aluminum scandium nitride (AlScN) on 4H-silicon carbide (SiC). This material system combines high sound velocity, high thermal conductivity, and strong piezoelectric response. Furthermore, the acoustoelectric effect (AE) was harnessed to achieve nonreciprocal RF amplification. A proof-of-concept AE delay line was realized by integrating Sezawa-mode SAWs in AlScN/SiC with ion-implanted SiC, supporting future applications for energy efficient RF amplifiers.

BE Doctoral Dissertation Defense: Brian-Tinh D. Vu
2025-09-13 10:00 - 11:00
The Department of Bioengineering at the University of Pennsylvania and Dr. Chamith Rajapakse are pleased to announce the Doctoral Dissertation Defense of Brian-Tinh D. Vu.
Title: “Rapid, High-resolution, and Signal-efficient Methods for the Clinical Translation of Bone Magnetic Resonance Imaging”
Date: Wednesday, September 17th
Time: 10:00 AM
Location: BRB 1412
Zoom: https://upenn.zoom.us/j/99746435550?pwd=[REDACTED]
The public is welcome to attend.
